Systems designed to solve speech processing tasks (e.g., speech or speaker recognition, language identification, emotion detection) are known to be affected by the recording conditions of the acoustic signal being processed. These conditions include nuisance characteristics that can interfere with the system's ability to process the acoustic signal in the desired manner, such as channel effects, background noise, reverberation, signal-to-noise ratio, language, speaker mood, and other characteristics that are unrelated to the characteristics one may want to detect. For instance, language variations are a nuisance when attempting to detect speaker identity, while speaker variations are a nuisance when attempting to detect language.
Knowledge of the nuisance characteristics present in the signal can be used to improve the performance of the system, since this knowledge can be used to predict the optimal parameters of the system under the detected nuisance characteristics. In some cases, the nature of the nuisance characteristics is known a priori, but in most practical cases, it is not. Conventional solutions used to automatically detect the characteristics of an acoustic signal are designed for a specific type of effect (e.g., noise, reverberation, language, type of channel, etc.). Thus, these solutions are helpful when it is know that the acoustic signal will contain only certain types of nuisance characteristics, and a different detector will typically be needed to detect each type of known nuisance characteristic.